Department of Computer Science, College of Computer Science, and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam 31441, Saudi Arabia.
School of Computer Science, National College of Business Administration & Economics, Lahore 54000, Pakistan.
Comput Intell Neurosci. 2022 Jul 31;2022:6852845. doi: 10.1155/2022/6852845. eCollection 2022.
According to the World Health Organization (WHO) report, heart disease is spreading throughout the world very rapidly and the situation is becoming alarming in people aged 40 or above (Xu, 2020). Different methods and procedures are adopted to detect and diagnose heart abnormalities. Data scientists are working on finding the different methods with the required accuracy (Strodthoff et al., 2021). Electrocardiogram (ECG) is the procedure to find the heart condition in the waveform. For ages, the machine learning techniques, which are feature based, played a vital role in the medical sciences and centralized the data in cloud computing and having access throughout the world. Furthermore, deep learning or transfer learning widens the vision and introduces different transfer learning methods to ensure accuracy and time management to detect the ECG in a better way in comparison to the previous and machine learning methods. Hence, it is said that transfer learning has turned world research into more appropriate and innovative research. Here, the proposed comparison and accuracy analysis of different transfer learning methods by using ECG classification for detecting ECG Arrhythmia (CAA-TL). The CAA-TL model has the multiclassification of the ECG dataset, which has been taken from Kaggle. Some of the healthy and unhealthy datasets have been taken in real-time, augmented, and fused with the Kaggle dataset, i.e., Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH dataset). The CAA-TL worked on the accuracy of heart problem detection by using different methods like ResNet50, AlexNet, and SqueezeNet. All three deep learning methods showed remarkable accuracy, which is improved from the previous research. The comparison of different deep learning approaches with respect to layers widens the research and gives the more clarity and accuracy and at the same time finds it time-consuming while working with multiclassification with massive dataset of ECG. The implementation of the proposed method showed an accuracy of 98.8%, 90.08%, and 91% for AlexNet, SqueezeNet, and ResNet50, respectively.
根据世界卫生组织（WHO）的报告，心脏病在全球范围内迅速蔓延，40 岁及以上人群的情况令人担忧（Xu，2020）。人们采用不同的方法和程序来检测和诊断心脏异常。数据科学家正在努力寻找具有所需准确性的不同方法（Strodthoff 等人，2021）。心电图（ECG）是发现心脏状况的波形的过程。长期以来，基于特征的机器学习技术在医学科学中发挥了至关重要的作用，将数据集中在云计算中，并在全球范围内访问。此外，深度学习或迁移学习拓宽了视野，并引入了不同的迁移学习方法，以确保准确性和时间管理，以便与之前的机器学习方法相比更好地检测 ECG。因此，有人说迁移学习使世界研究变得更加恰当和创新。在这里，通过使用 ECG 分类来检测 ECG 心律失常（CAA-TL），对不同的迁移学习方法进行了比较和准确性分析。CAA-TL 模型对 ECG 数据集进行了多分类，这些数据集是从 Kaggle 中获取的。实时获取了一些健康和不健康的数据集，对其进行了扩充，并与 Kaggle 数据集融合，即麻省理工学院-贝斯以色列医院（MIT-BIH 数据集）。CAA-TL 采用 ResNet50、AlexNet 和 SqueezeNet 等不同方法来提高心脏问题检测的准确性。所有三种深度学习方法都表现出了显著的准确性，比之前的研究有所提高。不同深度学习方法在层数方面的比较拓宽了研究范围，同时提供了更高的清晰度和准确性，但在处理 ECG 的大规模数据集的多分类时，也会发现它很耗时。该方法的实现表明，AlexNet、SqueezeNet 和 ResNet50 的准确率分别为 98.8%、90.08%和 91%。
